スポンサーリンク
Faculty of Engineering Miyazaki University | 論文
- Wide-Input-Range Four-Quadrant Analog Multiplier Using Floating-Gate MOSFET's
- A Gradient Ascent Learning Algorithm for Elastic Nets
- Relationship between Mechanical Absorption and Birefringence of Regenerated Cellulose Solid in Solvent
- Shallow Carrier Trap Levels in GaAsN Investigated by Photoluminescence
- Microscopic Study of Optical Potentials between Light-Heavy Ions(Microscopic Cluster Models of Light Nuclei and Related Topics)
- Analysis of Rolling of Bars by the Energy Method Using Finite Element Division : (Analysis of Square-Oval Pass)
- Analysis of Rolling of Angles by Energy Method Using Finite Element Division : Solid-Mechanics, Strength of Materials
- Analysis of Rolling of Bars by the Energy Method Using Finite Element Division
- Improvement of the Energy Method Using Finite Element Division and Application to Flat-rolling
- Inhibition of RuBisCO Cloned from Thermosynechococcus vulcanus and Expressed in Escherichia coli with Compounds Predicted by Molecular Operation Environment (MOE)(ENZYMOLOGY, PROTEIN ENGINEERING, AND ENZYME TECHNOLOGY)
- Chain Conformation of Deacetylated Beijeran Calcium Salt
- Crystalline Features of Streptococcal (1→3)-α-D-Glucans of Human Saliva
- Conformation of Poly[(1→3)-α-D-Maltotriose], a Major Part of the Elsinan Molecule, Studied by X-Ray Diffraction Coupled with Conformational Analysis
- Statistical estimation of thermocharacteristic properties of liquid HeliumII and superfluid heat pipe
- Isolation of Apoptosis- and Differentiation-Inducing Substances toward Human Promyelocytic Leukemia HL-60 Cells from Leaves of Juniperus taxifolia
- Effect of the Photoquenching of EL2 in GaAs Substrate on the Piezoelectric Photothermal and Surface Photovoltage Spectra of a GaAs Single Quantum Well Confined by GaAs/AlAs Short-Period Superlattices
- Developmet of Perceptron Simulator for Drag Design (PSDD)
- Multi-valued extention of AND/OR network
- Analogue type neural network expansions
- Swayed Reconstruction of BP-learning for multi-layer nerual networks